Your search

The identification of barriers for e-commerce to thrive in specific countries is a topic of great interest. This work proposes two models to study the barriers to B2C e-commerce adoption in Portugal, highlighting obstacles less exploited by previous research: the impact of offline shopping pleasure and the influence of the distance to shopping malls on online shopping intent. An online survey was conducted based on different constructs. A multivariate OLS hierarchical regression was used to analyse the proposed models regarding the intention to buy online and the number of online purchases. The results revealed that customer satisfaction is a strong predictor of intent to buy online and that perceived product risk remains a barrier to e-commerce. Consumers living in high urbanised areas have more propensity to buy online. Helpful information is provided regarding the impact of context, culture, product, and individual barriers, showing that multichannel strategies are best suited for success.

Hydrology modeling became a relevant topic for the Cidade da Praia, Cabo Verde, Africa, due to negative impact risk to local population and its assets. The modeling via Geographical Information Systems (GIS) can help the decision-making process of space occupation and characterization for this type of risk. Under the municipalities of Praia, the phenomenon of flash flood is common, causing soil erosion and landslide. This constitutes a risk for the local habitat, particularly in districts with a lack of strong human infrastructures. To simulate, analyze and generate risk maps using GIS to help this county governance authorities for decision-making, thus, becomes the main aim of this article.

Hydrology modeling became a relevant topic for the Cidade da Praia, Cabo Verde, Africa, due to negative impact risk to local population and its assets. The modeling via Geographical Information Systems (GIS) can help the decision-making process of space occupation and characterization for this type of risk. Under the municipalities of Praia, the phenomenon of flash flood is common, causing soil erosion and landslide. This constitutes a risk for the local habitat, particularly in districts with a lack of strong human infrastructures. To simulate, analyze and generate risk maps using GIS to help this county governance authorities for decision-making, thus, becomes the main aim of this article.

Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.

Objective: Over the past decade, arbitration has grown in popularity as a method of resolving commercial disputes worldwide. However, this practice is relatively new in Macao SAR. Recently, official plans were announced to make Macao as a seat of arbitration for commercial disputes between China and Portuguese-speaking countries (Hereinafter PSCs). This article is dedicated to explores the possibility of Macao undertaking and implementing such a role. Accordingly, this article addresses the following issues: What are the strengths and weaknesses of Macao as a seat and eventually as venue for hosting international commercial arbitration between Chinese and PSCs entrepreneurs?Methodology: A mixed-method approach of legal doctrinal and empirical research was used in this article. We first included a thorough study of the concept of arbitration followed by analysis of various legal journals and legislations, including Macao, China, and PSCs’ arbitration laws. An empirical research was then used to collect data by surveying and interviewing with both lawyers and arbitration practitioners from Macao, China and PSCs.Results: This article argues that the strength of Macao resides in the similarities between its legal system and that of the China and PSCs and the languages advantage (Chinese and Portuguese both official languages). In spite of this, arbitration is still relatively underutilized in the region, and there is a limited number of arbitrators and legal professionals with bilingual proficiency.Contributions: This article contributes to the identification of the opportunities and challenges that Macao faces in its potential future development as a seat/venue of arbitration between China and the PSCs.

This article explores how state and society relations have been affected by the development of information technology in China over the past 20 years. It argues that despite all the transformative changes that such technology has helped bring about, ?benefits? have to be weighed in terms of both empowerment of society and strengthening of state capacity. Ultimately, the digital challenge has not translated into a weakening of the authoritarian state, and this can be explained by the very nature of the party-state in China and how it has managed to make use of communication tools that prove to be both constructive and divisive.

In the last few years, the tourism industry has experienced rapid expansion and diversification, making it one of the fastest-growing financial industries in the world. Consequently, the hotel industry has significantly affected the environment's long-term viability. Many hotels have begun voluntarily implementing environmentally sustainable practices as they become more aware of their ecological footprint. There has been a great deal of discussion about the effects of hotel operations on the environment and tourism sustainability in Macau. It is because of these negative impacts that hoteliers have adopted green practices in an attempt to minimize them. By developing sustainability reports, hotels can set goals, measure performance, and manage change, resulting in better sustainability. It could also be viewed as a strategy to enhance the company’s sustainability reporting to ensure stakeholders know what the company does. The objective of this study is twofold based on the analysis of the official sustainability reports of four major hotel chains. Firstly, seven categories of sustainable practices effectively adopted by these chain hotels are identified and clusterized. Second, it is presented in which areas some hotels performed more efficiently than others, considering the UN Sustainable Development Goals (SDGs) as a reference. The results allow a comprehensive clusterized analysis of the industry in a highly developed gaming and entertainment area of South China and create a clear comparison between relevant players and their concerns about sustainability practices.

In the paper carried out by Wenjun et al. [Phys. Rev. A 95, 032124 (2017)], a generalization of the James effective dynamics theory based on a first version of the James method was presented. However, we contend that this is not a very rigorous way of deriving the effective third-order expansion for an interaction Hamiltonian with harmonic time-dependence. In fact, here we show that the third-order Hamiltonian obtained by Wenjun et al. is not Hermitian for general situations when we consider time dependence. Its non-Hermitian nature arises from the foundation of the theory itself. In this comment paper, the most general expression of the effective Hamiltonian expanded up to third order is obtained. Our derived effective Hamiltonian is Hermitian even in situations where we have time dependence.

In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]

In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.

Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.

Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.

We report the initial findings of an ongoing, long-term investigation into subjective quality of life in Macau, a Special Administrative Region of China. Data were collected via quarterly public surveys (2007 to 2009; n = 8,230), as part of the Macau Quality of Life Report. The main aims of the study were to: (a) ascertain the public’s satisfaction with life and with the regional situation in Macau; (b) confirm the utility of the International Wellbeing Index (IWI) as a measure of subjective life quality; and (c) contribute to ongoing discussion in the literature on quality of life in China. The data indicated moderate levels of personal (PWI = 64.4; range 63–66.7) and national (NWI = 59.7; 57.4–63.7) wellbeing across the study period, which implies that residents in Macau are generally satisfied with life. The lowest scores were reported in the first quarter of 2009, a period of great economic uncertainty in Macau and the world, but were positioned within the normative range. The IWI demonstrated good psychometric performance, consistent with previous studies in China and the West, which confirmed its utility. These findings are discussed in relation to the IWI’s theoretical underpinnings and the literature.

This chapter explores Quality of Work Life (QWL) in Macau. We investigate the meanings and importance of QWL and its implications in terms of happiness and business performance. Although QWL is central to people’s lives, research on this topic is still in its infancy in Macau. Our interviews revealed three salient themes of QWL: Work context, the perceived benefits and demands of the job; Organization, mainly work environment and factors within the organizational context mediating QWL; and the implications of QWL on overall living and happiness.

The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.

If Australia has been subject to major influences by the United States and European countries, why is its economy healthier than their counter partners? What are the economic foundations that underline this anti-counter cycle of financial worldwide crisis from Australia? What are some of the lessons that countries from Europe that have not fared during the current financial worldwide crisis should learn from Australia? The purpose of this paper is to review the present Australian management system. Four changes are identified including embracement of corporate governance, a shift to adopt more R&D activities, a shift to adopt environmental sustainability practices and emerging corporate social responsibility. On the conclusions settings, a recap and recommendation on how Portugal, a member of the PIGS (Portugal, Italy, Greece and Spain) Southern European Countries club forgot to embrace directives that have been applied in Australia, to avoid the actual financial and identity crisis.

The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.